3D Cloud reconstruction through geospatially-aware Masked Autoencoders

Anna Jungbluth; Emiliano Diaz Salas-Porras; Emmanuel Johnson; Guiseppe Castiglione; Joppe Massant; Kyriaki-Margarita Bintsi; Lilli Freischem; Michael Eisinger; Stella Girtsou; William Jones

REVIEW 2 major objections 5 minor 1 cited by

3D Cloud reconstruction through geospatially-aware Masked Autoencoders

T0 review · 2 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Geospatially-aware masked autoencoders, pre-trained on unlabelled MSG/SEVIRI imagery and fine-tuned on matched CloudSat radar profiles, reconstruct 3D cloud volumes with lower reflectivity error than the U-Net state of the art.

desk verdict A credible slice-reconstruction result wrapped in an unverified 3D volume claim; the pre-training gains are plausible, but the paper must validate off-track voxels before the title is justified. read the letter →

arxiv 2501.02035 v1 pith:AGX32WWQ submitted 2025-01-03 cs.CV cs.AI

Stella Girtsou , Emiliano Diaz Salas-Porras , Lilli Freischem , Joppe Massant , Kyriaki-Margarita Bintsi , Guiseppe Castiglione , William Jones , Michael Eisinger

show 2 more authors

Emmanuel Johnson Anna Jungbluth

This is my paper · ORCID

classification cs.CVcs.AI

keywords 3DcloudreconstructionMaskedAutoencoderSatMAEgeostationarysatelliteimagerySat/CPRradarreflectivityself-supervisedlearningverticalprofilesclimatemodeling

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that self-supervised pre-training on unlabelled geostationary satellite images makes a masked-autoencoder model better at reconstructing 3D cloud structure from a single top-down view than the previous supervised U-Net approach. Using MSG/SEVIRI imagery for pre-training and matched CloudSat/CPR radar profiles for fine-tuning, the authors report that a ViT-small model with 8x8 tokenization reaches 3.22 dBZ RMSE, and adding date, time, and coordinate encodings (the SatMAE variant) reaches 3.18 dBZ, versus 3.73 dBZ for the U-Net. The gains are concentrated in exactly the regions where error is most consequential for climate: thick cloud types and the tropical convection belt. If right, the method points to a way to turn the continuous, cheap stream of geostationary images into real-time three-dimensional cloud products using sparse radar labels.

What carries the argument

The load-bearing mechanism is the Masked Autoencoder (MAE) pretraining objective applied to multispectral geostationary imagery, with a Vision Transformer (ViT) encoder that tokenizes 11-channel 256x256 patches into 8x8 tokens and reconstructs a randomly masked 75% of them. The SatMAE variant extends this by injecting date/time and latitude/longitude encodings into the tokens, which the paper argues lets the model distinguish regions and diurnal patterns when predicting cloud profiles. After pre-training, the image decoder is replaced by a transposed-convolution regression head that produces a 90x256x256 reflectivity cube, and fine-tuning uses an MSE loss computed only on the single vertical CloudSat overpass slice inside each image without giving the model the slice's location.

What would settle it

Run the fine-tuned SatMAE model on test images whose CloudSat overpass is held out entirely, and compare its predicted reflectivity at pixels far from the training-track locations against actual CloudSat measurements of those regions: if off-track RMSE is substantially higher than the reported on-track 3.18 dBZ, the full-volume reconstruction claim fails.

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Extended reading notes

Core claim

The central discovery, on the paper's own terms, is that self-supervised masked image modeling transfers across data modalities: a transformer encoder trained only to reconstruct missing pixels of unlabelled MSG/SEVIRI frames, when fine-tuned on matched image-radar pairs, predicts CloudSat radar reflectivity profiles more accurately than a U-Net trained end-to-end from scratch on those same pairs. With ViT-small and 8x8 tokens the fine-tuned MAE reports mean reflectivity RMSE 3.22 +/- 1.68 dBZ and SSIM 0.84, while SatMAE with time-and-coordinate encoding reports 3.18 +/- 1.67 dBZ and SSIM 0.84; both beat the U-Net baseline (3.73 +/- 1.97 dBZ, SSIM 0.82). The authors additionally report that the superiority of MAE over U-Net and of SatMAE over MAE has p-value below 0.001 under a null distribution that preserves spatial correlation, and that coordinate encoding accounts for most of SatMAE's advantage. The model outputs a full 90x256x256 volume per image, trained without being told where the CloudSat track lies within the frame.

Load-bearing premise

The argument assumes that optimizing the loss on the one vertical CloudSat radar curtain that happens to cross each image teaches the network to reconstruct clouds reliably throughout the rest of the 90x256x256 volume, but no quantitative evaluation of off-track predictions is given.

Editorial extensions

If this is right

If the reported gains hold, self-supervised pre-training on unlabelled geostationary imagery becomes a practical way to compensate for the 16-day revisit and 1.4 km swath of radar cloud profilers.
A single 11-channel MSG image suffices at inference time to produce a 90x256x256 reflectivity volume, so the method can in principle provide near-real-time 3D cloud data without waiting for a radar overpass.
The geospatial encoding effect implies that location-aware representations matter for cloud reconstruction: a model that knows where and when an image was taken generalises better, especially in tropical convection.
The cloud-type breakdown indicates the largest remaining errors are in precipitating cloud classes (nimbostratus and deep convection), so the method's value for climate applications may hinge on improving those categories.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the reported metrics only audit the thin radar curtain, so either an off-track evaluation or a volumetric metric is needed before treating these numbers as a 3D reconstruction accuracy; a natural test is to spatially split training and test tracks by corridor and compare performance as a function of distance from the nearest training track.
Editorial inference: because pre-training consumes only unlabelled full-disk imagery, the same recipe should transfer to other geostationary instruments (for example, GOES-R or Himawari) and to future lidar or radar profiles without any new labelling cost.
Editorial inference: the paper's token-size comparison suggests that spatial resolution of the patch grid is a first-order design choice; mixing 8x8 tokens at cloud boundaries with coarser tokens elsewhere could buy the observed detail at a fraction of the computational cost.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The real result here is modest but believable: pre-training a ViT with MAE or SatMAE on unlabeled SEVIRI imagery, then fine-tuning on CloudSat overpass slices, improves reflectivity RMSE, PSNR, and SSIM over the U-Net baseline in Table 1. The improvements are consistent, the temporal split is sensible, and the SatMAE ablation isolating time and coordinate encodings is a nice touch. For the narrow task of predicting the CloudSat curtain from a geostationary image, the empirical case holds up.

What does not hold up is the 3D framing. The loss is computed only on the one vertical CloudSat slice, and the model is not told where that slice is. That is a reasonable way to force some generalization, but it is not evidence that the full 90 x 256 x 256 output volume is physically meaningful. Every reported metric, every figure, and the hypothesis test all evaluate only the track. The paper's own assertion that this setup 'forces the model to reasonably reconstruct 3D volumes anywhere' is a hope, not a result. Off-track validation—even on a handful of CloudSat overpasses held out from a different region or time—is needed before the title '3D Cloud reconstruction' is defensible. This is the load-bearing issue.

There is also a sloppy statistics appendix: the text says 5,112 trials but the binomial null uses n = 46,752. That is likely a typo, but it undermines confidence in the reported p < 0.001. The bootstrap nearest-neighbor procedure is creative but described too tersely to be checked without code. And there is no code release, despite the 'upon acceptance' promise.

Minor issues: the metrics in Table 1 are the mean and standard deviation across test samples, not model uncertainty, as the paper itself notes—fine, but the error bars are therefore not what readers might assume. The single year of data is a scope limitation, not a flaw, given the compute budget.

The paper is aimed at the atmospheric remote sensing and applied ML crowd. If I worked on cloud profile estimation, I would want to read it, but I would not cite the 3D claim until the off-track evaluation exists. A serious referee could turn this into a solid paper by demanding that evaluation and corrected statistics. I would send it out.

Referee Report

2 major / 5 minor

Summary. The paper proposes using masked autoencoders (MAE) and a geospatially aware variant (SatMAE) pre-trained on unlabelled MSG/SEVIRI imagery, then fine-tuned on matched MSG/SEVIRI-CloudSat/CPR radar reflectivity pairs, to reconstruct 3D cloud volumes. The central empirical claim is that pre-training improves reconstruction over a supervised U-Net baseline, with the SatMAE variant achieving the best reflectivity RMSE, PSNR, SSIM, and F1 scores on a temporally split test set. The authors further report that the improvements are statistically significant with p<0.001 and are most pronounced in the tropical convection belt. The paper includes implementation details, training curves, example reconstructions, and a cloud-type breakdown of errors.

Significance. If the empirical claims hold, the paper would demonstrate a practical benefit of self-supervised pre-training and geospatial encoding for vertical cloud profile estimation from geostationary imagery, a task relevant to climate modeling and satellite remote sensing. The work uses an external U-Net baseline, temporally separated train/validation/test splits, and reports standard metrics on a held-out test period, which are commendable. The main advertised contribution, however, is 3D cloud reconstruction, and the evaluation only measures performance on the supervised CloudSat curtain; this limits the strength of the significance claim until off-track generalization is quantified.

major comments (2)

[Appendix A, Hypothesis test] The hypothesis test contains an internal inconsistency: the text says '5,112 trials (the number observations in our test set)' but then specifies a binomial distribution with n = 46,752. These numbers must be reconciled, and the discrepancy directly affects the reported p<0.001. In addition, the empirical null is constructed by using the 1000 nearest neighbours of each location as a bootstrap sample, which assumes that spatial correlation decays over a smaller scale than the scale of the maps in Figure 4; this assumption should be stated and justified. Please correct the test description and report the p-values with the exact sample size used.
[Section 3, Geospatial Awareness; Figure 4] Figure 4 and its caption state that the RMSE maps include 'our entire dataset (i.e. including training, validation, and test examples).' The claim that SatMAE provides the largest improvements in the tropical convection belt is therefore supported by data that includes the model's own training and validation examples, not only held-out data. To support the generalization claim, the spatial error maps should be recomputed on the test split only, or the text should clearly indicate that the tropical-belt improvement is a qualitative observation that includes non-test data.

minor comments (5)

[Figure 7 caption] The caption contains a typo: 'reconstPetersonruction' should read 'reconstruction'.
[Abstract and Section 1] The phrase 'state-of-the-art methods like U-Nets' is stronger than what is evaluated; the comparison is to a single U-Net baseline from Brüning et al. Consider wording such as 'a U-Net baseline from prior work' to be precise.
[Tables 2 and 3] The model names are inconsistent: 'SatMAE' and 'SATMAE' are both used. Please standardize the capitalization.
[Appendix A, Hypothesis test] The bootstrap procedure is underspecified: please describe exactly how the bootstrap sample is formed from the 1000 nearest neighbours and how the empirical null distribution is estimated from it.
[Section 2, Data & Method] The data dimensions are given as '11 x 256 x 256 pixels' and '90 x 256 x 256 (H x W x L)', but the ordering of height/width/length is not clearly defined for the input image; please state the channel and spatial dimension order consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claim is an empirical benchmark comparison on held-out test days, not a derivation from its own inputs.

full rationale

The paper's derivation chain is an empirical comparison, not a mathematical derivation. The central claim that MAE/SatMAE pre-training plus time and coordinate encodings improves fine-tuned reconstruction of CloudSat reflectivity profiles relative to a U-Net is supported by held-out test metrics (Table 1, Figures 3 and 4, Appendix A) computed on temporally separated data splits. The MAE and SatMAE citations supply architecture components, not the paper's conclusion, and no load-bearing premise is justified solely by a self-citation. The '3D reconstruction' generalization claim is weaker than the evidence: the loss and all reported quantitative metrics are computed only on the CloudSat curtain, so off-track voxels are never evaluated, and the hypothesis test contains an inconsistency between 5,112 trials and n=46,752. These are external-validity and statistical-reporting concerns, not circularity. No fitted parameter is renamed as a prediction, no result is equivalent to its inputs by construction, and no uniqueness theorem or prior-work assertion forces the chosen model. The comparison is self-contained against the external Brüning et al. U-Net baseline on held-out days.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. The central comparison is empirical; the model is a standard ViT/MAE with added positional encodings. The main postulates are domain assumptions about data validity and the slice-supervision strategy, plus a set of hand-picked training hyperparameters.

free parameters (6)

Minimum cloud cover threshold for CloudSat track inclusion = 20%
Tracks with at least 20% cloud cover are kept; this hand-chosen threshold shapes the training distribution and biases the dataset toward cloudy scenes.
Height levels cropped from CloudSat profiles = 25 bottom, 10 top
The bottom 25 and top 10 of the 125 vertical bins are removed to discard artifacts and clear air, following prior work; this changes the target definition.
Mask ratio during MAE pre-training = 75%
The standard MAE masking fraction from the cited MAE paper, chosen by hand, controls the difficulty of the auxiliary reconstruction task.
Pre-training patch subsampling fraction = 10% per epoch
A different random 10% of the 1.2 million patches is used each epoch to reduce compute; this affects which unlabeled data the encoder is exposed to.
Fine-tuning pair sampling fraction = 50% per epoch
Randomly sampling half of the image-profile pairs each epoch affects the model's exposure to rare cloud types and the effective dataset size.
Number of training epochs for fine-tuning = 50
All models were trained for 50 epochs, which the paper says yielded the highest perceptual quality; the comparison is tied to this early-stopping choice.

assumptions (5)

domain assumption CloudSat/CPR radar reflectivity profiles are a valid target for 3D cloud structure.
The learning task treats reflectivity in 125 vertical bins as ground truth; no independent validation of this proxy is offered beyond prior usage.
domain assumption Spatiotemporal alignment between MSG/SEVIRI images and CloudSat overpasses is accurate enough that the paired profiles represent the same clouds.
Clouds evolve on minute timescales and the two instruments do not observe simultaneously; Section 2 assumes the matching procedure yields valid training pairs.
ad hoc to paper A loss computed on one vertical slice can supervise a full 3D volume reconstruction.
In Section 2, the model outputs a 90 x 256 x 256 volume but the loss is computed only where the CloudSat track crosses the image, and the model is not told where that is.
ad hoc to paper The empirical null distribution preserves spatial correlation through 1000-nearest-neighbor bootstrapping.
Appendix A assumes the magnitude of correlation at the 1000-location scale is larger than at larger scales; if false, the reported p-values are invalid.
standard math ViT, MAE, and SatMAE architectures behave as described in their source papers.
The paper builds on the cited MAE and SatMAE works without re-deriving the architectures.

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Cite this review

Pith. "Pith review of 3D Cloud reconstruction through geospatially-aware Masked Autoencoders." pith.science (2026). https://pith.science/paper/AGX32WWQ

@misc{pith2026250102035,
  author       = {Pith},
  title        = {Pith review of: 3D Cloud reconstruction through geospatially-aware Masked Autoencoders},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AGX32WWQ}},
  note         = {Machine review of arXiv:2501.02035}
}

read the original abstract

Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction.

Figures

Figures reproduced from arXiv: 2501.02035 by the authors.

**Figure 2.** (a) Validation Loss for the different size MAEs. (b) Visualization of the masked, recon [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Comparison of example MSG/SEVIRI input channels ( [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Monthly and yearly root-mean-square errors (RMSE) across MSG’s field-of-view (one [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Mean squared error (MSE) loss, peak signal-to-noise ratio (PSNR), and structural similarity [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Root-mean-square errors (RMSE) across MSG’s field-of-view (one value per image-profile [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

**Figure 7.** Figure 7: Example 3D cloud reconstPetersonruction. The left figure shows an MSG/SEVIRI input [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: Root-mean-square error (RMSE; in dBZ) as a function of cloud type for the different [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

C3DIR: A Deep Learning 3-Dimensional Cloud Property Retrieval Scheme for Passive Satellite Imagers
physics.ao-ph 2026-07 conditional novelty 6.0 of 10

C3DIR is a single multi-sensor deep-learning model that retrieves 3-D ice, liquid, and rain water content from passive imagers using voxel-to-voxel collocation with active-sensor profiles.

Reference graph

Works this paper leans on

14 extracted references · 10 canonical work pages · cited by 1 Pith paper

[1]

Determination of cloud vertical structure from upper- air observations

Junhong Wang and William B Rossow. “Determination of cloud vertical structure from upper- air observations”. In: Journal of Applied Meteorology and Climatology 34.10 (1995), pp. 2243– 2258

work page 1995
[2]

The EarthCARE mission–science and system overview

Tobias Wehr et al. “The EarthCARE mission–science and system overview”. In:Atmospheric Measurement Techniques 16.15 (2023), pp. 3581–3608

work page 2023
[3]

A 3D cloud-construction algorithm for the EarthCARE satellite mission

H. W. Barker et al. “A 3D cloud-construction algorithm for the EarthCARE satellite mission”. In: Quarterly Journal of the Royal Meteorological Society 137.657 (Apr. 2011), pp. 1042–1058. ISSN : 1477-870X. DOI: 10.1002/qj.824. URL: http://dx.doi.org/10.1002/qj.824

work page doi:10.1002/qj.824 2011
[4]

Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product

Zhipeng Qu et al. “Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product”. In: Atmospheric Measurement Techniques 16.9 (May 2023), pp. 2319–2331. ISSN : 1867-8548. DOI: 10.5194/amt-16-2319-2023 . URL: http://dx.doi.org/10.5194/amt-16-2319-2023

work page doi:10.5194/amt-16-2319-2023 2023
[5]

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Sarah Brüning, Stefan Niebler, and Holger Tost. “Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data”. en. In: Atmos. Meas. Tech. 17.3 (Feb. 2024), pp. 961–978

work page 2024
[6]

IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

Kai Jeggle et al. IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision. 2023. DOI: 10.48550/ARXIV.2310.03499. URL: https: //arxiv.org/abs/2310.03499

work page Pith review arXiv doi:10.48550/arxiv.2310.03499 2023
[7]

Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network

Jussi Leinonen, Alexandre Guillaume, and Tianle Yuan. “Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network”. In: Geophysical Research Letters 46.12 (June 2019), pp. 7035–7044. ISSN : 1944-8007. DOI: 10.1029/2019gl082532. URL: http: //dx.doi.org/10.1029/2019GL082532

work page doi:10.1029/2019gl082532 2019
[8]

Masked Autoencoders Are Scalable Vision Learners

Kaiming He et al. Masked Autoencoders Are Scalable Vision Learners . 2021. arXiv: 2111. 06377 [cs.CV]. URL: https://arxiv.org/abs/2111.06377

arXiv 2021

Show all 14 references

[9]

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery

Yezhen Cong et al. SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. 2023. arXiv: 2207.08051 [cs.CV] . URL: https://arxiv.org/abs/ 2207.08051

2023 arXiv
[10]

Adam: A method for stochastic optimization

Diederik P. Kingma and Jimmy Lei Ba. “Adam: A method for stochastic optimization”. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. 2015

2015
[11]

Learning representations by back-propagating errors

David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. “Learning representations by back-propagating errors”. In: Nature 323.6088 (1986), pp. 533–536. ISSN : 00280836. DOI: 10.1038/323533a0

1986 doi
[12]

Image quality assessment: from error visibility to structural similarity

Zhou Wang et al. “Image quality assessment: from error visibility to structural similarity”. In: IEEE Transactions on Image Processing 13.4 (2004), pp. 600–612. DOI: 10.1109/TIP.2003. 819861. 7 A Appendix Further Training Details. We conducted our experiments on 1-2 NVIDIA V10...

2004 doi
[13]

construct Yi ∈ {0, 1}, i = 1, ...,5, 112 that determines which model is superior at location i in the following way Yi = 1 {Wi<m} where: • 1 x is the indicator function • Wi = Z 1 i − Z 2 i • Z k i is the test rmse observed at location i for model k and, • m is the test observ...
[14]

We ideally would like to have many independent samples of Yi and then calculate, Pi, the proportion of times each model is superior at a location i. Since we only have one observation of each Yi we obtain a boostrap sample by using Yi and Yj for j ∈ N N1000(i) where N N1000(i)...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Determination of cloud vertical structure from upper- air observations

Junhong Wang and William B Rossow. “Determination of cloud vertical structure from upper- air observations”. In: Journal of Applied Meteorology and Climatology 34.10 (1995), pp. 2243– 2258

work page 1995

[2] [2]

The EarthCARE mission–science and system overview

Tobias Wehr et al. “The EarthCARE mission–science and system overview”. In:Atmospheric Measurement Techniques 16.15 (2023), pp. 3581–3608

work page 2023

[3] [3]

A 3D cloud-construction algorithm for the EarthCARE satellite mission

H. W. Barker et al. “A 3D cloud-construction algorithm for the EarthCARE satellite mission”. In: Quarterly Journal of the Royal Meteorological Society 137.657 (Apr. 2011), pp. 1042–1058. ISSN : 1477-870X. DOI: 10.1002/qj.824. URL: http://dx.doi.org/10.1002/qj.824

work page doi:10.1002/qj.824 2011

[4] [4]

Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product

Zhipeng Qu et al. “Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product”. In: Atmospheric Measurement Techniques 16.9 (May 2023), pp. 2319–2331. ISSN : 1867-8548. DOI: 10.5194/amt-16-2319-2023 . URL: http://dx.doi.org/10.5194/amt-16-2319-2023

work page doi:10.5194/amt-16-2319-2023 2023

[5] [5]

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Sarah Brüning, Stefan Niebler, and Holger Tost. “Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data”. en. In: Atmos. Meas. Tech. 17.3 (Feb. 2024), pp. 961–978

work page 2024

[6] [6]

IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

Kai Jeggle et al. IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision. 2023. DOI: 10.48550/ARXIV.2310.03499. URL: https: //arxiv.org/abs/2310.03499

work page Pith review arXiv doi:10.48550/arxiv.2310.03499 2023

[7] [7]

Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network

Jussi Leinonen, Alexandre Guillaume, and Tianle Yuan. “Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network”. In: Geophysical Research Letters 46.12 (June 2019), pp. 7035–7044. ISSN : 1944-8007. DOI: 10.1029/2019gl082532. URL: http: //dx.doi.org/10.1029/2019GL082532

work page doi:10.1029/2019gl082532 2019

[8] [8]

Masked Autoencoders Are Scalable Vision Learners

Kaiming He et al. Masked Autoencoders Are Scalable Vision Learners . 2021. arXiv: 2111. 06377 [cs.CV]. URL: https://arxiv.org/abs/2111.06377

arXiv 2021